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Most traditional models of uncertainty have focused on the associational relationship among variables as captured by conditional dependence. In order to successfully manage intelligent systems for decision making, however, we must be able…

Artificial Intelligence · Computer Science 2015-05-19 David Heckerman , Ross D. Shachter

Most of the stochastic orders for comparing random variables, considered in the literature, are afflicted with two main drawbacks: (i) lack of connex property and (ii) lack of consideration of any dependence structure between the random…

Methodology · Statistics 2021-03-03 Sugata Ghosh , Asok K. Nanda

Ambiguity-averse decision makers typically dislike not only the presence of ambiguous events but also their increase, contrary to what standard ambiguity models predict. We axiomatically study such a decision maker. She avoids ex ante…

Theoretical Economics · Economics 2026-05-25 Yutaro Akita , Kensei Nakamura

This paper studies preference aggregation under ambiguity when agents have incomplete preference relations due to imprecise beliefs. We introduce the "dual" of the Pareto principle, which respects unanimity among individuals, including…

Theoretical Economics · Economics 2025-12-19 Leo Kurata , Kensei Nakamura

We show that it is possible to understand and identify a decision maker's subjective causal judgements by observing her preferences over interventions. Following Pearl [2000], we represent causality using causal models (also called…

Theoretical Economics · Economics 2024-01-23 Joseph Y. Halpern , Evan Piermont

We elicit incomplete preferences over monetary gambles with subjective uncertainty. Subjects rank gambles, and these rankings are used to estimate preferences; payments are based on estimated preferences. About 40\% of subjects express…

General Economics · Economics 2022-10-06 Kirby Nielsen , Luca Rigotti

We study mechanism which operate on ordinal preference information (i.e., rank ordered lists of alternatives) on the full domain of weak preferences that admits indifferences. We present a novel decomposition of strategyproofness into three…

Computer Science and Game Theory · Computer Science 2020-07-15 Timo Mennle , Sven Seuken

LLMs are increasingly used to make or support high-stakes decisions under uncertainty, where alignment depends not only on factual accuracy but on how models weigh tradeoffs between different outcomes. We present an empirical pipeline for…

Machine Learning · Computer Science 2026-05-12 Khurram Yamin , Jingjing Tang , Eric Horvitz , Bryan Wilder

In earlier work, we introduced the framework of language-based decisions, the core idea of which was to modify Savage's classical decision-theoretic framework by taking actions to be descriptions in some language, rather than functions from…

Logic in Computer Science · Computer Science 2023-07-18 Adam Bjorndahl , Joseph Y. Halpern

Preferences among acts are analyzed in the style of L. Savage, but as partially ordered. The rationality postulates considered are weaker than Savage's on three counts. The Sure Thing Principle is derived in this setting. The postulates are…

Computer Science and Game Theory · Computer Science 2007-05-23 Daniel Lehmann

Emergent collective group processes and capabilities have been studied through analysis of transactive memory, measures of group task performance, and group intelligence, among others. In their approach to collective behaviors, these…

Physics and Society · Physics 2019-01-01 Yaneer Bar-Yam , David Kantor

The primary theme of this investigation is a decision theoretic account of conditional ought statements (e.g., "You ought to do A, if C") that rectifies glaring deficiencies in classical deontic logic. The resulting account forms a sound…

Artificial Intelligence · Computer Science 2013-03-08 Judea Pearl

We consider a simple model of imprecise comparisons: there exists some $\delta>0$ such that when a subject is given two elements to compare, if the values of those elements (as perceived by the subject) differ by at least $\delta$, then the…

Data Structures and Algorithms · Computer Science 2015-01-14 Miklos Ajtai , Vitaly Feldman , Avinatan Hassidim , Jelani Nelson

Evidence-based decision-making entails collecting (costly) observations about an underlying phenomenon of interest, and subsequently committing to an (informed) decision on the basis of accumulated evidence. In this setting, active sensing…

Machine Learning · Statistics 2020-06-26 Daniel Jarrett , Mihaela van der Schaar

A multicriteria group choice problem is considered in the paper. The model includes a set of feasible alternatives, a vector criterion, and n preference relations of the decision makers (DMs). Each preference relation is a cone relation…

Optimization and Control · Mathematics 2019-05-14 Alexey O. Zakharov

We introduce a new incremental preference elicitation procedure able to deal with noisy responses of a Decision Maker (DM). The originality of the contribution is to propose a Bayesian approach for determining a preferred solution in a…

Artificial Intelligence · Computer Science 2020-07-30 Nadjet Bourdache , Patrice Perny , Olivier Spanjaard

The way that people make choices or exhibit preferences can be strongly affected by the set of available alternatives, often called the choice set. Furthermore, there are usually heterogeneous preferences, either at an individual level…

Computer Science and Game Theory · Computer Science 2020-08-04 Kiran Tomlinson , Austin R. Benson

We propose and axiomatize preferences on a product state space in light of uncertainty regarding the dependency of different payoff-relevant factors. Dependence structures allow to decompose probabilities and allow to pin down behavior…

Theoretical Economics · Economics 2026-05-28 Gerrit Bauch , Lorenz Hartmann

We show that many bounded rationality patterns of choice can be alternatively represented as testable models of limited consideration, and we elicit the features of the associated unobserved consideration sets from the observed choice.…

Theoretical Economics · Economics 2024-03-08 Davide Carpentiere , Angelo Petralia

This paper is dedicated to a cautious learning methodology for predicting preferences between alternatives characterized by binary attributes (formally, each alternative is seen as a subset of attributes). By "cautious", we mean that the…

Artificial Intelligence · Computer Science 2022-06-16 Hugo Gilbert , Mohamed Ouaguenouni , Meltem Ozturk , Olivier Spanjaard
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